69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
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import pandas as pd
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def load_and_process():
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# cords - mapping zip codes to long/lat coordinates
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cords = pd.read_csv("../data/raw/zip_lat_long.csv")
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## counties - Relating US counties to their long/lat position on the Earth
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# Combine the county name with the state code
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def combine_name_state(row):
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row["name"] = f"{row['name']} {row['STUSAB']}"
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return row
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counties = (
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pd.read_csv("../data/raw/us-county-boundaries.csv", sep=";")
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.rename({
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"NAME": "name",
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"INTPTLAT": "lat",
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"INTPTLON": "long",
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}, axis="columns")
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.apply(combine_name_state, axis="columns")
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.drop(["STUSAB"], axis="columns")
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)
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## pol - Election results from the 2012 American presidential election
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def combine_name_state(row):
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row["county"] = f"{row['county']} {row['state']}"
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return row
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pol = (
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pd.read_csv("../data/raw/countypres_2000-2020.csv")
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.query("`year` == 2012")
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.reset_index()
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.drop([
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"year", "state", "county_fips", "office",
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"candidate", "version", "mode", "index",
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], axis="columns")
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.rename({
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"county_name": "county",
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"state_po": "state",
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"candidatevotes": "votes",
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"totalvotes": "total"
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}, axis="columns")
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.apply(lambda x: x.str.capitalize() if x.name == "county" or x.name == "party" else x)
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.apply(combine_name_state, axis="columns")
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.merge(counties, left_on="county", right_on="name")
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.drop(["state", "name"], axis="columns")
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.assign(percent=lambda x: x.votes/x.total)
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)
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## gb - the gaybourhoods dataset
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gb = (
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pd.read_csv("../data/raw/gaybourhoods.csv")
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.merge(cords, left_on="GEOID10", right_on="ZIP") \
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.drop([
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"Mjoint_MF", "Mjoint_SS", "Mjoint_FF", "Mjoint_MM",
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"Cns_TotHH", "Cns_UPSS", "Cns_UPFF", "Cns_UPMM",
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"ParadeFlag", "FF_Tax", "FF_Cns", "MM_Tax", "MM_Cns",
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"SS_Index_Weight", "Parade_Weight", "Bars_Weight",
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"GEOID10", "ZIP",
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], axis="columns") \
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.rename({
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"LAT": "lat",
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"LNG": "long",
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}, axis="columns")
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)
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return (gb, pol, counties, cords)
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